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In this paper, we investigate a multi-carrier multi-relay cooperative system with amplify-and-forward relaying over frequency-selective underwater acoustic channels. Relay selection rules rely on the maximization of signal-to-noise ratio (SNR). We basically use two distinct relay selection schemes; one bases on SNR-summation and the other bases on selecting the relay which gives the best SNR among...
In this paper, we compare some of the existing joint state particle filtering algorithms for closely spaced target tracking problem. Both maximum a posteriori (MAP) and minimum mean square error (MMSE) estimation outputs of four different algorithms are compared. We also include comparison of a non-joint state particle filter and Kalman filter for a baseline. Simulation results show that claimed performance...
Most of the state-of-the-art reinforcement learning algorithms are based on Bellman equations and make use of fixed-point iteration methods to converge to suboptimal solutions. However, some of the recent approaches transform the reinforcement learning problem into an equivalent likelihood maximization problem with using appropriate graphical models. Hence, it allows the adoption of probabilistic...
In this paper, a minimum bit-error rate (MBER) linear multi-user detection (MUD) algorithm for time-varying frequency-selective fading direct-sequence code-division multiple-access (DS-CDMA) channels is proposed. The BER cost function of the MUD is highly non-linear and may have several local minimum. It is shown that with some appropriate constraints the MBER MUD problem is equivalent to a constrained...
In this paper, approximation methods for binary polar code construction proposed by Tal and Vardy are extended to non-binary source alphabets. Additionally, a new approximation method that enables accurate polar code construction with less usage of computational resources is proposed. Efficiency and accuracy of proposed methods are supported analytically and numerically.
Graphs are important mathematical tools for modelling processes. An important issue in this area is to infer the changes that occur in the underlying generative process. In this work, inference of multiple change points in stochastic block graph time series is studied. A well-known algorithm for inference in time series is the forward-backward algorithm. In order to decrease computational complexity...
In this work, the curve compression problem is approached with a model-based probabilistic framework. We propose three different models. The proposed models can be used for purposes such as feature extraction or compression. The first model we propose is basically a Bayesian regression model for fitting piece-wise defined segments. The second model unifies clustering with regression. The third model...
This work presents approximation methods that are used to identify Bit Error Rate (BER) expressions which are frequently utilized in investigation and comparison of performance for wireless communication systems in the literature. In this group of approximation methods, some expectation integrals, which are complicated to analyze and time-consuming to evaluate through Monte Carlo simulations, are...
In this paper, we present an infinite mixture model to partition short time series data. Components of this mixture model are piecewise linear sequences. The model is constructed using Chinese restaurant process and the posterior distribution over the sample assignments are calculated using collapsed Gibbs sampling. A piecewise linear sequence is represented by fewer parameters than its observations...
In this paper, statistical modeling of clutter data measured by a noncoherent S-band marine radar mounted on a fixed position is presented. Characterization is done by finding the best fitted density function to the clutter over eight candidate distribution. Real-time parameter estimation of the predetermined distribution and automatic threshold detection for Constant False Alarm Rate (CFAR) is provided.
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